{"id":"W2122918123","doi":"10.1504/ijhvs.2011.037961","title":"Crashworthiness improvement of a pickup truck's chassis frame using the Pareto-Front and genetic algorithm","year":2011,"lang":"en","type":"article","venue":"International Journal of Heavy Vehicle Systems","topic":"Cellular and Composite Structures","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Chassis; Crashworthiness; Truck; Engineering; Automotive engineering; Flexibility (engineering); Frame (networking); Genetic algorithm; Multi-objective optimization; Relation (database); Optimal design; Automotive industry; Pareto principle; Set (abstract data type); Pickup; Structural engineering; Computer science; Finite element method; Mechanical engineering; Mathematical optimization; Mathematics; Aerospace engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001344849,0.0001110974,0.0002003572,0.000096173,0.00003238823,0.00004886093,0.000288868,0.00004745422,0.00002081924],"category_scores_gemma":[0.000006208805,0.00007760702,0.00008442048,0.00004007297,0.00004103602,0.00009047865,0.00004063133,0.00014957,5.741786e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006535711,"about_ca_system_score_gemma":0.00002332314,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003143049,"about_ca_topic_score_gemma":0.000009291587,"domain_scores_codex":[0.9989097,0.00002643554,0.0005039044,0.00007376299,0.0003738221,0.0001124122],"domain_scores_gemma":[0.999372,0.00002568126,0.0002044667,0.0001124274,0.0002304533,0.00005495575],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004019196,0.0002909038,0.07734384,0.0005215532,0.004506197,0.0006301448,0.01505317,0.07949703,0.5446553,0.0006138143,0.0004609591,0.2760252],"study_design_scores_gemma":[0.002465295,0.0005574253,0.1438148,0.0007909641,0.0002429483,0.003372083,0.002113021,0.743582,0.09947782,0.001216966,0.001772049,0.0005945633],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9491956,0.002737209,0.04599213,0.00002674215,0.001832335,0.00009494532,0.00001047381,0.00001080737,0.00009976649],"genre_scores_gemma":[0.9977987,0.00008057802,0.001717422,0.00001552512,0.0003605106,0.000002208151,6.43975e-7,0.00001586693,0.000008552296],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.664085,"threshold_uncertainty_score":0.3164722,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01209040773117019,"score_gpt":0.2172989293649782,"score_spread":0.205208521633808,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}